Production well productivity influence factor analysis method, system and equipment and storage medium

Abstract

The embodiment of the invention relates to the technical field of oil and gas field development, and discloses a production well productivity influence factor analysis method, system and device and a storage medium, and the method comprises the steps: building a production well productivity evaluation model; analyzing the fracturing database of the production well, and determining the correlation degree between each influence factor influencing the productivity of the production well and the productivity of the production well; sequencing the influence factors according to the correlation degree; the first n influence factors are selected to be input into the production well productivity evaluation model, the evaluation result of the production well productivity is output, and n is larger than or equal to 1; according to the embodiment of the invention, the grey correlation analysis method is used for analyzing the fracturing database of the block, finding out the main parameters influencing the productivity of the oil well and the influence degree and rule of the parameters, and sorting the main parameters according to the correlation degree, so that the main control factors influencing the productivity of the horizontal well of the compact conglomerate reservoir and the weights of the main control factors are determined; and further obtaining the productivity change rule of the compact conglomerate oil reservoir horizontal well.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the technical field of oil and gas field development, and in particular to a method, system, device and storage medium for analyzing factors affecting production well productivity. Background Art

[0002] In recent years, exploration in the Mahu area of ​​the Junggar Basin in Xinjiang has made continuous breakthroughs, showing good resource prospects and becoming the main area for crude oil production capacity construction in Xinjiang oil fields. Horizontal wells plus volume fracturing is an economical and effective way to develop oil fields. Horizontal well fracturing technology has been widely used in low permeability and unconventional reservoirs. Compared with other tight oils in China, the tight conglomerate reservoirs in the Mahu area have complex rock structures, strong heterogeneity, low precision in describing sweet spots, and quite complex changes in the production laws of horizontal wells. It is necessary to analyze the production laws and determine the main influencing factors, and then formulate targeted development plans and measures to ensure high production of horizontal wells. According to statistical analysis, there are many factors that affect the production capacity of horizontal wells. From the single factor analysis of each influencing parameter and the oil well production capacity, it can be seen that the degree of influence of each parameter on the oil well production capacity cannot be distinguished under the single factor analysis. Therefore, there is a problem of how to distinguish the influence of each parameter on the oil well production capacity. Summary of the invention

[0003] The embodiments of the present disclosure provide a method, system, device and storage medium for analyzing factors affecting production well productivity to solve or alleviate one or more of the above technical problems in the prior art.

[0004] According to one aspect of the present disclosure, a method for analyzing factors affecting production well productivity is provided, comprising:

[0005] Establish a production well productivity evaluation model;

[0006] Analyze the fracturing database of production wells to determine the correlation between various factors affecting the production capacity of production wells and the production capacity of production wells;

[0007] Sort the influencing factors according to the degree of correlation;

[0008] Select the first n influencing factors and input them into the production well capacity evaluation model, and output the evaluation result of the production well capacity, n≥1.

[0009] In a possible implementation, the establishment of a production well productivity evaluation model includes:

[0010] Calculate the oil production per meter of each type of oil layer;

[0011] Divide the thickness of each oil layer by the oil production per meter of the corresponding oil layer to make the production capacity of each oil layer equivalent;

[0012] Calculate the thickness of various oil layers according to their production capacity;

[0013] Establish multiple regression models;

[0014] The converted oil layer thickness is input into the multivariate regression model to form a production well productivity evaluation model.

[0015] In one possible implementation, the factors affecting the production capacity of production wells include geological factors and engineering factors. The geological factors include horizontal section length, oil layer thickness, permeability, formation pressure and porosity; the engineering factors include fracturing stage, fracture length, fracturing fluid and sand addition amount.

[0016] In a possible implementation, the analysis of the fracturing database of the production wells to determine the correlation between various factors affecting the production capacity of the production wells and the production capacity of the production wells includes:

[0017] Determine the analysis sequence: Determine the production well capacity data parent sequence X0 and the influencing factor data subsequence X i (i=1,2,…,n), i represents the i-th factor affecting production capacity, forming the analysis sequence matrix: In the formula, N means that there are N values ​​in the i-th influencing factor, n means there are n factors affecting production capacity, and X n (N) indicates the size of the Nth value in the nth influencing factor;

[0018] Dimensionless processing of original data: Find the average value of each sequence, divide the average value by each original data in the corresponding sequence, and the obtained data column is recorded as the averaged sequence: In the formula, i represents the i-th factor affecting production capacity, k represents the k-th moment value of the i-th influencing factor, N represents the number of N values ​​in the i-th influencing factor, and x′ i (k) represents the k-th moment value of the i-th sequence of factors affecting capacity divided by the mean of the i-th sequence of factors affecting capacity;

[0019] Calculate the absolute value Δ of the difference between the parent sequence and each subsequence at each time point 0i (k):Δ 0i (k)=|X0(k)-X i (k)|, where: Δ 0i (k) is |X at time k i -X0|value, k represents the k-th moment value in the i-th influencing factor;

[0020] Calculate the relationship between the parent sequence X0 and each subsequence X at each time point i The correlation coefficient is calculated as follows: i=0, 1,...,n; k=1,2,...,N, where ε 0i(k) represents the parent sequence X0 and each subsequence X i The correlation coefficient, Δ 0i (k) represents |X at time k i -X0| value, △min represents the minimum difference between the parent sequence and the subsequence, △max represents the maximum difference between the parent sequence and the subsequence, and ρ is the resolution coefficient;

[0021] Solve for the correlation coefficient matrix: Where: ε 0i (k) represents the relationship between the parent sequence X0 and each subsequence X i The correlation coefficient, ε 0n (N) represents the correlation coefficient between the parent sequence X0 and the nth subsequence, and N represents that there are N values ​​in the i-th influencing factor;

[0022] Solving the correlation degree: Calculate the correlation degree of the two sequences by taking the average value of the correlation coefficients of the two sequences at each moment. The calculation formula is: In the formula, γ i represents the correlation between the parent sequence and the i-th influencing factor, N represents that there are N values ​​in the i-th influencing factor, ε 0i (k) represents the parent sequence X0 and each subsequence X i The correlation coefficient, k represents the k-th moment value of the i-th influencing factor;

[0023] Solving parameter weights: Use the correlation to calculate the weights of each influencing factor. The calculation formula is: Where W i represents the weight of the i-th influencing factor, γ i It represents the correlation between the parent sequence and the i-th influencing factor, and n represents that there are n factors affecting production capacity.

[0024] According to one aspect of the present disclosure, a production well productivity influencing factor analysis system is provided, comprising:

[0025] Establishing a unit for establishing a production well productivity evaluation model;

[0026] A determination unit, used to analyze the fracturing database of the production wells and determine the correlation between various factors affecting the production capacity of the production wells and the production capacity of the production wells;

[0027] A sorting unit is used to sort the influencing factors according to the magnitude of the correlation;

[0028] The evaluation unit is used to select the first n influencing factors to input into the production well capacity evaluation model, and output the evaluation result of the production well capacity, n≥1.

[0029] In a possible implementation, the establishing unit includes:

[0030] The first calculation module is used to calculate the oil production per meter of each type of oil layer;

[0031] The equivalent module is used to divide the thickness of each oil layer by the oil production per meter of the corresponding oil layer to make the production capacity of each oil layer equivalent;

[0032] Conversion module, used to convert the thickness of various oil layers according to the production capacity of various oil layers;

[0033] Build a module for building multiple regression models;

[0034] A module is formed, which is used to input the converted oil layer thickness into a multivariate regression model to form a production well capacity evaluation model.

[0035] In one possible implementation, the factors affecting the production capacity of production wells include geological factors and engineering factors. The geological factors include horizontal section length, oil layer thickness, permeability, formation pressure and porosity; the engineering factors include fracturing stage, fracture length, fracturing fluid and sand addition amount.

[0036] In a possible implementation manner, the determining unit includes:

[0037] Determination module, used to determine the analysis sequence: determine the production well capacity data parent sequence X0 and the influencing factor data subsequence X i (i=1,2,…,n), i represents the i-th factor affecting production capacity, forming the analysis sequence matrix: In the formula, N means that there are N values ​​in the i-th influencing factor, n means there are n factors affecting production capacity, and X n (N) indicates the size of the Nth value in the nth influencing factor;

[0038] The processing module is used for dimensionless processing of the original data: the average value of each sequence is calculated, and the data column obtained by dividing the average value by each original data in the corresponding sequence is recorded as the averaged sequence: In the formula, i represents the i-th factor affecting production capacity, k represents the k-th moment value of the i-th influencing factor, N represents the number of N values ​​in the i-th influencing factor, and x′ i (k) represents the k-th moment value of the i-th sequence of factors affecting capacity divided by the mean of the i-th sequence of factors affecting capacity;

[0039] The second calculation module is used to calculate the absolute value Δ of the difference between the parent sequence and each subsequence at each time point 0i (k):Δ 0i (k)=|X0(k)-X i (k)|, where: Δ 0i (k) is |X at time k i-X0|value, k represents the k-th moment value of the i-th influencing factor;

[0040] The third calculation module is used to calculate the relationship between the parent sequence X0 and each subsequence X at each time point. i The correlation coefficient is calculated as follows: i=0, 1,...,n; k=1,2,...,N, where ε 0i (k) represents the relationship between the parent sequence X0 and each subsequence X i The correlation coefficient, Δ 0i (k) represents |X at time k i -X0| value, △min represents the minimum difference between the parent sequence and the subsequence, △max represents the maximum difference between the parent sequence and the subsequence, and ρ is the resolution coefficient;

[0041] The first solution module is used to solve the correlation coefficient matrix: Where: ε 0i (k) represents the relationship between the parent sequence X0 and each subsequence X i The correlation coefficient, ε 0n (N) represents the correlation coefficient between the parent sequence X0 and the nth subsequence, and N represents that there are N values ​​in the i-th influencing factor;

[0042] The second solution module is used to solve the correlation degree: the correlation degree of the two sequences is calculated by the average value of the correlation coefficients of the two sequences at each moment. The calculation formula is: In the formula, γ i represents the correlation between the parent sequence and the i-th influencing factor, N represents that there are N values ​​in the i-th influencing factor, ε 0i (k) represents the relationship between the parent sequence X0 and each subsequence X i The correlation coefficient, k represents the k-th moment value of the i-th influencing factor;

[0043] The third solution module is used to solve the parameter weight: the weight of each influencing factor is calculated using the correlation degree, and the calculation formula is: Where W i represents the weight of the i-th influencing factor, γ i It represents the correlation between the parent sequence and the i-th influencing factor, and n represents that there are n factors affecting production capacity.

[0044] According to one aspect of the present disclosure, a device for analyzing factors affecting production well productivity is provided, comprising:

[0045] Processor and memory;

[0046] The memory is used to store a computer program, and the processor calls the computer program stored in the memory to execute any of the above-mentioned methods for analyzing factors affecting production well productivity.

[0047] According to one aspect of the present disclosure, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, the processor is enabled to execute any of the above-mentioned methods for analyzing factors affecting production well productivity.

[0048] The exemplary embodiments of the present disclosure have the following beneficial effects: the exemplary embodiments of the present disclosure propose a method for evaluating the productivity of horizontal wells in tight conglomerate reservoirs taking into account the influence of multiple factors, combining qualitative and quantitative methods on the basis of accurate correlation, and using the grey correlation analysis method to analyze the fracturing database of the block, find out the main parameters affecting the productivity of the oil wells and the degree of influence and laws of these parameters, and sort them according to the size of the correlation, so as to optimize the main influencing factors of the fracturing effect, clarify the main controlling factors affecting the productivity of horizontal wells in conglomerate tight oil reservoirs and their weights, and then derive the law of changes in the productivity of horizontal wells in tight conglomerate oil reservoirs.

[0049] The details of one or more embodiments of the present application are presented in the following drawings and descriptions. Other features and advantages of the present application will become apparent from the accompanying drawings. It should be understood that the above general description and the detailed descriptions below are only exemplary and explanatory and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0051] Figure 1 is a flow chart of a method for analyzing factors affecting production well productivity of the exemplary embodiment;

[0052] Figure 2 is a schematic diagram of multiple regression analysis of this exemplary embodiment;

[0053] Figure 3 is a schematic diagram of the relationship between the daily oil production of the production well and the average permeability×effective thickness of the exemplary embodiment;

[0054] Figure 4 1 is a schematic diagram of the relationship between the daily oil production of a production well in 180 days and the effective oil layer thickness in this exemplary embodiment;

[0055] Figure 5 3. This is a schematic diagram of the relationship between the daily oil production of a production well and the effective oil layer thickness for 360 days in this exemplary embodiment;

[0056] Figure 6 is a schematic diagram of the relationship between daily oil production and oil pressure of a vertical well in this exemplary embodiment;

[0057] Figure 7 is a flowchart of the grey relational analysis of this exemplary embodiment;

[0058] Figure 8 is a schematic diagram of a physical model of horizontal well seepage in this exemplary embodiment;

[0059] Fig. 9 is a cross-plot of equivalent encountered oil layer length and initial production in this exemplary embodiment;

[0060] Fig.10 is a cross-plot of equivalent encountered oil layer length and peak production in this exemplary embodiment;

[0061] Fig.11 is a cross-plot of the equivalent encountered oil layer length and the one-year cumulative average production in this exemplary embodiment;

[0062] Fig.12 is a schematic diagram of the experimental results of stress sensitivity evaluation of the experimental well in this exemplary embodiment;

[0063] Fig.13 is a block diagram of a production well productivity influencing factor analysis system of the exemplary embodiment;

[0064] Fig.14 It is a schematic structural diagram of a production well productivity influencing factor analysis device according to this exemplary embodiment. DETAILED DESCRIPTION

[0065] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as being limited to the examples set forth herein; on the contrary, these embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concepts of the example embodiments are fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0066] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware units or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0067] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the steps. For example, some steps may be decomposed, while some steps may be combined or partially combined, so the actual execution order may change according to the actual situation.

[0068] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example.

[0069] In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or sub-modules is not necessarily limited to those steps or sub-modules explicitly listed, but may include other steps or sub-modules not explicitly listed or inherent to these processes, methods, products, or apparatuses.

[0070] Figure 1 is a flow chart of a method for analyzing factors affecting production well productivity of this exemplary embodiment. Figure 1 As shown, an exemplary embodiment of the present disclosure provides a method for analyzing factors affecting production well productivity, including:

[0071] S1 Establish a production well capacity evaluation model;

[0072] S2 analyzes the fracturing database of the production wells to determine the correlation between various factors affecting the production capacity of the production wells and the production capacity of the production wells;

[0073] S3 sorts the influencing factors according to the degree of correlation;

[0074] S4 selects the first n influencing factors and inputs them into the production well capacity evaluation model, and outputs the evaluation result of the production well capacity, where n≥1.

[0075] Exemplarily, S1 establishes a production well productivity evaluation model including:

[0076] S10 calculates the oil production per meter of each type of oil layer;

[0077] S11 divides the thickness of each oil layer by the oil production per meter of the corresponding oil layer to make the production capacity of each oil layer equivalent;

[0078] S12 calculates the thickness of various oil layers according to their production capacity;

[0079] S13 builds a multivariate regression model;

[0080] S14 inputs the converted oil layer thickness into a multivariate regression model to form a production well capacity evaluation model.

[0081] Figure 2 is a schematic diagram of multiple regression analysis of this exemplary embodiment, such as Figure 2 As shown in the figure, multiple regression analysis refers to a statistical analysis method that regards one variable as the dependent variable and one or more other variables as independent variables, establishes a linear or nonlinear mathematical model quantitative relationship between multiple variables, and uses sample data for analysis. Multiple regression model: Y = β0 + β1X1 + β2X2 + ... + β k X k +e; where Y is the dependent variable, X1, X2, …, X k is the independent variable, e is a constant, β0, β1, β2, …, β k is the coefficient of the independent variable.

[0082] β0, β1, β2, …, β k are called regression parameters, and the parameters are calculated by least squares estimation.

[0083] In this embodiment, the main / secondary factors of the productivity of conglomerate tight oil reservoirs were analyzed based on the multivariate regression analysis method for 80 production wells in the Ma 18 well area.

[0084] Since the positive correlation coefficient between oil layer thickness and average daily production is higher and it is the main factor affecting the productivity of horizontal wells, the data of oil layer thickness and average daily oil production are selected to establish the model.

[0085] Oil layer thickness conversion:

[0086] There are four types of oil layers in the well area, but the contribution rate of each type of oil layer to the oil layer and their respective production capacity are different. In order to find the relationship between each type of oil layer and daily oil production, that is, the contribution rate to production capacity, it is necessary to make the production capacity of each type of oil layer equivalent in order to calculate its contribution capacity.

[0087] From the data of 80 production wells, we select wells with a large proportion of Class I oil layers, so that the influence of the thickness of Class II and below oil layers can be ignored. We divide the daily oil production by the thickness of Class I oil layers to calculate the number of tons of oil produced per meter of Class I oil layers. We find that Class I oil layers can produce 0.17 tons of oil per meter.

[0088] From the data of 80 production wells, we selected wells with a large proportion of Class II oil layers, so that the influence of the thickness of oil layers below Class III can be ignored. However, the production caused by Class I oil layers must be considered. The daily oil production minus the oil production of Class I oil layers is divided by the thickness of Class II oil layers to calculate the tons of oil produced per meter of Class II oil layers. It is found that Class II oil layers can produce 0.07 tons of oil per meter.

[0089] From the data of 80 production wells, we selected wells with a large proportion of Class III oil layers, so that the influence of the thickness of Class IV oil layers can be ignored. The production of Class I and Class II cannot be ignored. The daily oil production minus the oil pressure production of Class I and Class II oil layers is divided by the thickness of Class III oil layers to calculate the oil production of Class III oil layers per meter of 0.05 tons. By analogy, the oil production of Class IV oil layers per meter is 0.04 tons.

[0090] Divide the thickness of each oil layer by its oil production capacity per meter to make their oil production capacity equivalent, and substitute the converted oil layer thickness into the multiple regression formula for calculation. The multiple regression calculation results are shown in Table 1 (in Table 1, Coefficients represents regression coefficients, t stat represents the t value of the statistic in the T test, P-value represents the P value corresponding to the T test, Lower 95% and Upper 95% represent the lower and upper limits of the 95% confidence interval, Intercept represents the intercept, and Variable represents the variable):

[0091] Table 1

[0092]

[0093]

[0094] The relevant formula is derived from this: Y = 3.22X1 2 +3.19X2 2 +2.75X3 2 +0.5X4 2 ; In the formula, Y is the oil production, X1 is the thickness of the first type of oil layer, X2 is the thickness of the second type of oil layer, X3 is the thickness of the third type of oil layer, and X4 is the thickness of the fourth type of oil layer.

[0095] The multiple correlation coefficient R, also known as the correlation coefficient, is used to measure the degree of correlation between the independent variables x and y. In this example, R = 0.8196 verifies that the relationship between the oil layer thickness and the average daily oil production is highly positively correlated.

[0096] The effective thickness converted according to the capacity of each type of oil layer = H1 + 0.41H2 + 0.29H3 + 0.24H4; where H is the total effective oil layer thickness converted according to the capacity, H1 is the effective oil layer thickness converted according to the capacity of the first type of oil layer, H2 is the effective oil layer thickness converted according to the capacity of the second type of oil layer, H3 is the effective oil layer thickness converted according to the capacity of the third type of oil layer, and H4 is the effective oil layer thickness converted according to the capacity of the fourth type of oil layer.

[0097] The positive correlation between the single factor permeability and the average daily oil production is not obvious. Figure 3 Schematic diagram of the relationship between the daily oil production of the production well and the average permeability × effective thickness of the exemplary embodiment. Figure 3 As shown in the figure, according to the production capacity formula, multiplying it with the effective thickness yields a positive correlation coefficient with the average daily oil production of 0.4918, indicating that the effective permeability and the effective oil layer thickness act together on the well production capacity.

[0098] Figure 4 This is a schematic diagram of the relationship between the daily oil production of a production well in 180 days and the effective oil layer thickness in this exemplary embodiment. Figure 5 360-day daily oil production of the production well and the effective oil layer thickness. Figure 4 and Figure 5 As shown in the figure, the relationship between the average daily oil production of 180 days of stable production and the average daily oil production of 360 days of stable production and the thickness of the oil layer is analyzed by using the multivariate regression analysis method, and the following is obtained:

[0099] Y180=0.544X1 2 +0.571X2 2 +0.266X3 2 +0.226X4 2 ;

[0100] Y360=0.711X1 2 +0.394X2 2 +0.121X3 2 +0.143X;

[0101] Where, Y180 is the average daily oil production for 180 days of stable production, X1 is the thickness of Class I oil layer, X2 is the thickness of Class II oil layer, X3 is the thickness of Class III oil layer, X4 is the thickness of Class IV oil layer, and Y360 is the average daily oil production for 360 days of stable production.

[0102] Figure 6 Schematic diagram of the relationship between daily oil production and oil pressure of a vertical well in this exemplary embodiment. Figure 6 As shown in the figure, the results of correlation analysis show that among the factor parameters, oil pressure, oil layer thickness, and the product of permeability and effective thickness have a greater correlation with the production capacity of tight oil reservoirs, and are the main factors affecting the production capacity, while the correlation between porosity, oil saturation, and permeability and the production capacity of tight oil reservoirs is smaller, and is a secondary factor affecting the production capacity.

[0103] Exemplarily, factors affecting the productivity of production wells include geological factors and engineering factors. Geological factors include horizontal section length, oil layer thickness, permeability, formation pressure and porosity; engineering factors include fracturing stage, fracture length, fracturing fluid and sand addition amount.

[0104] For example, S2 analyzes the fracturing database of the production wells to determine the correlation between various factors affecting the production capacity of the production wells and the production capacity of the production wells, including:

[0105] S20 determines the analysis sequence: determines the production well productivity data parent sequence X0 and the influencing factor data subsequence X i (i=1,2,…,n), i represents the i-th factor affecting production capacity, forming the analysis sequence matrix: In the formula, N means that there are N values ​​in the i-th influencing factor, n means there are n factors affecting production capacity, and X n (N) represents the size of the Nth value in the nth influencing factor.

[0106] S21 dimensionless processing of original data: find the average value of each sequence, divide the average value by each original data in the corresponding sequence, and the obtained data column is recorded as the averaged sequence: In the formula, i represents the i-th factor affecting production capacity, k represents the k-th moment value of the i-th influencing factor, N represents the number of N values ​​in the i-th influencing factor, and x′ i (k) represents the k-th moment value of the i-th sequence of factors affecting capacity divided by the mean of the i-th sequence of factors affecting capacity.

[0107] S11 calculates the absolute value Δ of the difference between the parent sequence and each subsequence at each time point 0i (k):Δ 0i (k)=|X0(k)-X i (k)|, where k represents the k-th moment value of the i-th influencing factor, Δ 0i (k) is |X at time k i -X0| value, that is, Δ 0i (k) represents the absolute value of the difference between the parent sequence and each subsequence at time k.

[0108] S22 calculates the relationship between the parent sequence X0 and each subsequence X at each time point i The correlation coefficient is calculated as follows: i=0,1,...,n; k=1,2,|,N; where, ε 0i (k) represents the relationship between the parent sequence X0 and each subsequence X i The correlation coefficient, Δ 0i (k) is |X at time k i-X0| value, that is, the absolute value of the difference between the parent sequence and each subsequence at time k, △min represents the minimum difference between the parent sequence and the subsequence, △max represents the maximum difference between the parent sequence and the subsequence, and ρ is the resolution coefficient.

[0109] S23 solves the correlation coefficient matrix: Where: ε 0i (k) represents the parent sequence X0 and each subsequence X i The correlation coefficient, ε 0n (N) represents the correlation coefficient between the parent sequence X0 and the nth subsequence, and N represents that there are N values ​​in the i-th influencing factor;

[0110] S24 solves the correlation degree: the correlation degree of the two sequences is calculated by taking the average value of the correlation coefficients of the two sequences at each moment. The calculation formula is: γ i represents the correlation between the parent sequence and the i-th influencing factor, N represents that there are N values ​​in the i-th influencing factor, ε 0i (k) represents the parent sequence X0 and each subsequence X i The correlation coefficient, k represents the k-th moment value of the i-th influencing factor;

[0111] S25 solves parameter weights: Use the correlation to calculate the weights of each influencing factor. The calculation formula is: Where W i represents the weight of the i-th influencing factor, γ i It represents the correlation between the parent sequence and the i-th influencing factor, and n represents that there are n factors affecting production capacity.

[0112] It is worth noting that the grey correlation degree is a quantity used to describe the closeness of the relationship between system factors and a measure of the system change trend. Generally speaking, the change trend of the quantifiable system can be characterized by the change trend of the sequence, and the change trend of each sequence always changes according to a certain magnitude and trend (referring to the shape of the curve). Therefore, the closeness of the relationship between system sequences is manifested as the similarity of magnitude change and development trend between the two. These are two different but mutually restrictive forms of grey correlation analysis. The change in magnitude can be measured by displacement difference (the distance between points), and the development trend can be measured by first-order or second-order slope difference. Therefore, the displacement difference and slope difference (speed, acceleration) can be used to characterize the correlation degree. At present, the calculation models for judging the magnitude of grey correlation between sequences are mainly the following: Deng's correlation degree, grey B-type correlation degree, C-type correlation degree, T-type correlation degree, generalized correlation degree, grey slope correlation degree, grey Euclidean correlation degree, etc.

[0113] The basic principle of grey correlation analysis is to seek the relationship between various factors in the system through a certain method, find out the important factors affecting the target parameters, and then grasp the main characteristics of things. The essence of grey correlation analysis is the quantitative description and comparison of the development and change trend of a system, which is the basis of grey system analysis, decision-making and prediction. Grey correlation analysis includes the selection of subsequences and parent sequences, the calculation of correlation coefficients and correlation degrees, and the calculation of correlation sequences and correlation matrices. The grey correlation analysis method is used to analyze the fracturing database of the block, determine the correlation degree of each influencing factor and sort them according to the size of the correlation degree, so as to select the main influencing factors of the fracturing effect. The main analysis steps are as follows: Figure 7 As shown, Figure 7 4 is a flowchart of the grey relational analysis of this exemplary embodiment.

[0114] In this embodiment, the correlation coefficient and the grey correlation degree calculation method include:

[0115] a) Determination of reference sequence:

[0116] The quantitative indicators arranged in a certain order are used to quantitatively reflect the nature of the object being judged, so as to obtain the relationship between the object being judged and the factors affecting it from the internal structure of the data information. This quantitative indicator is the parent sequence of grey relational analysis. It is recorded as:

[0117]

[0118] In the formula, n means that there are n factors that affect the productivity of the production well, and t ranges from 1 to n.

[0119] The ordered arrangement of factors that influence or determine the nature of the thing being judged is called a subsequence. Consider m sub-factors of the main factor. Then the subsequence is:

[0120]

[0121] In the formula, n means that there are n factors affecting the production capacity of the production well, t ranges from 1 to n, and m means the m sub-factors of each factor (subsequence).

[0122] b) Raw data processing:

[0123] Once the parent sequence and subsequence are determined, the original data matrix can be constructed:

[0124]

[0125] In the formula, n means that there are n factors affecting the production capacity of the production well, and m means the m sub-factors of each factor (subsequence).

[0126] The physical meanings of various influencing factors in the system are different, and the dimensions of the data are also different. Therefore, the original data must be converted into comparable data and the data dimensions must be eliminated. Usually, the two methods of homogenization and initialization are used.

[0127] c) Mean value expression:

[0128]

[0129] In the formula, n means that there are n factors affecting the productivity of the production well, t ranges from 1 to n, and i means the i-th factor affecting the productivity.

[0130] Initialization expression:

[0131]

[0132] In the formula, t ranges from 1 to n, and i represents the i-th factor affecting production capacity.

[0133] Generally speaking, if you only need to perform correlation analysis on the values ​​of the original series, you can use the mean transformation; while for more stable system series, the initialization transformation is usually used when performing correlation analysis on dynamic sequences.

[0134] d) Find the difference sequence and extreme value:

[0135] Let the transformed parent sequence be , and the transformed child sequence be . Then we can calculate the absolute difference and extreme value between the observation values ​​of the child factor and the parent factor at the same observation point.

[0136] The difference sequence is:

[0137] The minimum difference is:

[0138] The maximum difference is:

[0139] Where: t ranges from 1 to n, i represents the i-th factor affecting production capacity;

[0140] Δ t (i,0) is the absolute difference of a certain observation point compared with the sequence;

[0141] Δmin is the minimum absolute difference of all observation points in all comparison sequences, and Δmin is generally 0;

[0142] Δmax is the maximum absolute difference of all observation points in all comparison sequences;

[0143] e) Find the correlation coefficient:

[0144] The correlation coefficient between the parent sequence and the child sequence is Lt(i, 0):

[0145]

[0146] Where: △min represents the minimum difference between the parent sequence and the subsequence, △max represents the maximum difference between the parent sequence and the subsequence, ρ is the resolution coefficient, which is used to weaken the influence of distortion caused by the large value of the maximum absolute difference. The resolution coefficient can improve the significance of the difference between the correlation coefficients. Generally, ρ is 0.1 to 0.5.

[0147] f) Correlation calculation:

[0148] The correlation degree of each sub-factor to the parent factor r(i,0):

[0149]

[0150] In the formula, t ranges from 1 to n, i represents the i-th factor affecting productivity, and n represents that there are n factors affecting the productivity of the production well.

[0151] The correlation is a bounded number with a value range of (0.1, 1). The closer the correlation value is to 1, the closer the relationship between the sub-factor and the parent factor is, that is, the greater the influence of this sub-factor on the parent factor, and vice versa.

[0152] The correlation order is the order of the correlation between each subsequence and the parent sequence. If each subsequence is taken as the parent sequence in turn, and then its correlation with other sequences is calculated, the resulting matrix is ​​the correlation matrix.

[0153] g) Determination of primary and secondary factors of tight oil reservoir productivity:

[0154] The correlation analysis of factors affecting the productivity of tight oil reservoirs was conducted on 80 production wells. It can be seen that the correlation order is: oil pressure > effective oil layer thickness > effective oil layer thickness × effective permeability > porosity > oil saturation > permeability. The correlation results are: 0.7385 > 0.5541 > 0.4918 > 0.0828 > 0.0823 > 0.0668.

[0155] From the results of correlation analysis (see Table 2, Table 2 is the correlation coefficient table of geological factors), it can be seen that among the factor parameters, oil pressure, oil layer thickness, and the product of permeability and effective thickness have a greater correlation with the production capacity of tight oil reservoirs, and are the main factors affecting production capacity, while the correlation between porosity, oil saturation, and permeability and the production capacity of tight oil reservoirs is smaller, and is a secondary factor affecting production capacity.

[0156] Table 2

[0157] parameter Relevance Oil pressure 0.7385 Oil layer thickness 0.5541 Permeability × effective thickness 0.4918 Porosity 0.0828 Oil saturation 0.0823 Permeability 0.0668

[0158] In the process of oilfield development, horizontal wells have been widely used due to their advantages such as large oil drainage area, long production well section, and small bottom hole pressure drop. In order to better improve the productivity of horizontal wells and give full play to their development potential, it is necessary to further study the impact of various factors in the production process on their productivity and seek specific measures to improve the productivity of horizontal wells.

[0159] The factors that affect the productivity of horizontal wells can be summarized into two categories. The first category is geological factors, which mainly include horizontal section length, oil layer thickness, permeability, formation pressure, porosity, high-quality reservoirs, etc. The second category is engineering factors, including fracturing stages, fracture length, fracturing fluid, sand addition, etc. The horizontal well seepage model is the main physical model for understanding the underground production and development of horizontal wells. Figure 8 As shown, Figure 8 Schematic diagram of the physical model of horizontal well seepage in this exemplary embodiment.

[0160] This embodiment proposes the concept of equivalent oil layer length, equivalent oil layer length = 1.15 × Class I oil layer +

[0161] Class II oil layer + 0.85×Class III oil layer. Fig. 9 is a cross-plot of the equivalent encountered oil layer length and the initial production in this exemplary embodiment, Fig.10 is a cross-plot of equivalent encountered oil layer length and peak production in this exemplary embodiment, such as Fig. 9 and Fig.10 As shown in the figure, there is no correlation between the equivalent encountered oil layer length and the initial production and peak production.

[0162] Fig.11 is a cross-plot of the equivalent encountered oil layer length and the one-year cumulative average production in this exemplary embodiment, such as Fig.11 As shown in the figure, there is a very obvious positive correlation between the length of the oil layer encountered and the cumulative production in one year.

[0163] This embodiment uses the grey correlation analysis method to analyze the fracturing database of the block, determines the correlation of each influencing factor and sorts them according to the size of the correlation, so as to select the main influencing factors of the fracturing effect. The main analysis steps are as follows: Figure 7 shown.

[0164] In this embodiment, the grey correlation analysis theory is mainly carried out according to the following steps:

[0165] (1) Determine the analysis sequence: Based on the qualitative analysis of the research question, determine the dependent variable and independent variable. Suppose the dependent variable data parent sequence is X0 and the independent variable data subsequence is X i (i=1, 2, ..., n), forming the following analysis sequence matrix:

[0166] Analyze the sequence matrix:

[0167]

[0168] In the formula, N means that there are N values ​​in the i-th influencing factor, n means there are n factors affecting production capacity, and X n (N) indicates the size of the Nth value in the nth influencing factor;

[0169] (2) Dimensionless processing of raw data: The dimensions (or units) of various factors in the system are not necessarily the same, and it is difficult to directly compare absolute data. At the same time, there is also the problem of different geometric curve proportions.

[0170] Therefore, the original data needs to eliminate the dimension (or unit) and convert it into a comparable dimensionless series. The average value of each series should be calculated first, and then the average value is used to divide the original data in the corresponding series. The new data series obtained is the averaged series. Its characteristics are that the dimension is one, its value is greater than, and most of them are close to, and the series curves intersect each other.

[0171]

[0172] In the formula, i represents the i-th factor affecting production capacity, k represents the k-th moment value of the i-th influencing factor, N represents the number of N values ​​in the i-th influencing factor, and x′ i (k) represents the k-th moment value of the i-th sequence of factors affecting capacity divided by the mean of the i-th sequence of factors affecting capacity;

[0173] (3) Calculate the absolute value Δ of the difference between the parent sequence and each subsequence at each time point 0i (k), namely:

[0174]

[0175] In the formula, Δ 0i (k) is |X at time k i -X0|value, k represents the k-th moment value of the i-th influencing factor.

[0176] (4) Find the relationship between the parent sequence X0 and each subsequence X at each time point i The correlation coefficient is calculated

[0177] The formula is:

[0178]

[0179] In the formula, ε 0i (k) represents the parent sequence X0 and each subsequence X i The correlation coefficient, Δ 0i (k) represents |X at time k i-X0| value, △min represents the minimum difference between the parent sequence and the subsequence, △max represents the maximum difference between the parent sequence and the subsequence, and ρ is the resolution coefficient.

[0180] Solve for the correlation coefficient matrix:

[0181]

[0182] Where: △max is |X i -X0|'s maximum value; △min is |X i -X0| is the minimum value; ρ is the resolution coefficient, which is used to increase the significance of the difference between correlation coefficients. In general, it can be taken as 0.1 to 0.5, usually 0.5.

[0183] (5) Solve the correlation:

[0184] The essence of correlation analysis is to compare the geometric relationship of time series data. If the two sequences overlap at all time points, that is, the correlation coefficients are equal, then the correlation of the two sequences must also be equal. On the other hand, the comparison sequence cannot be vertical at any time, so the correlation coefficients are greater than, and therefore the correlation is also greater than. Therefore, the correlation of the two sequences is calculated by the average value of the correlation coefficients of the two sequences at each time, that is:

[0185]

[0186] In the formula, γ i represents the correlation between the parent sequence and the i-th influencing factor, N represents that there are N values ​​in the i-th influencing factor, ε 0i (k) represents the relationship between the parent sequence X0 and each subsequence X i The correlation coefficient, k represents the k-th moment value of the i-th influencing factor;

[0187] (6) Relevance ranking:

[0188] The degree of correlation between parameters is mainly described by the order of correlation, rather than the absolute value of correlation. Arranging the correlation of each subsequence to the same parent sequence in order of size forms a correlation order, which reflects the "superiority" relationship of each subsequence to the parent sequence. The greater the correlation, the more significant the importance of the influencing factors to the oil well productivity.

[0189] If γ0a>γ0b, then there is a parameter that has a greater influence on the oil well productivity than this parameter;

[0190] If γ0a<γ0b, then there is a parameter whose influence on oil well productivity is less than that of this parameter;

[0191] If γ0a=γ0b, then there is a parameter that has the same influence on the oil well productivity as this parameter;

[0192] If γ0a≥γ0b, then there is a parameter that has a greater impact on the oil well productivity than or equal to the parameter. If γ0a≤γ0b, then there is a parameter that has a lesser impact on the oil well productivity than or equal to the parameter.

[0193] The above shows the correlation degree of the γ0a subsequence and the correlation degree of the γ0b parent sequence.

[0194] (7) Solve the parameter weights:

[0195] In order to truly reflect the importance of a certain parameter to the oil well productivity in the overall evaluation, the weight quantitative distribution of each parameter is calculated using the correlation degree.

[0196]

[0197] The analysis results considering only geological factors are shown in Table 3, which is the correlation analysis result table:

[0198] Table 3

[0199] parameter Relevance Oil saturation 0.785 Horizontal segment length 0.725 Permeability 0.635 Equivalent oil layer length encountered 0.546 Porosity 0.507 Crude oil viscosity 0.480 Fluidity 0.459

[0200] Volume fracturing technology for horizontal wells is an important means of exploiting low permeability oil and gas reservoirs, and the production capacity of fractured horizontal wells is affected by many factors such as horizontal well engineering factors and development factors. Therefore, the influence of engineering factors and development factors on production capacity in tight conglomerate reservoirs is studied. When analyzing the development law of horizontal wells by engineering factors and development factors, the distribution of oil layers in which each well is fractured is different, and there are differences in the classification of high-quality oil layers and oil layers in which the fracturing perforation clusters are located. Therefore, it is necessary to group and analyze the production law of each horizontal well, and determine the classification standard based on oil layer classification / length of oil layers encountered. Among them, the drilling rate of oil layers in Class I oil wells is greater than 40%, and the drilling rate of oil layers in Class I and Class II is greater than 80%; the drilling rate of oil layers in Class II oil wells is less than 40%, and the drilling rate of oil layers in Class I and Class II is greater than 80%; the drilling rate of oil layers in Class III oil wells is less than 80%. The specific classification results of horizontal wells are shown in Table 4, which is the horizontal well classification data table.

[0201] Table 4

[0202]

[0203]

[0204] By analyzing the engineering factors that affect the development of horizontal wells, it can be concluded that the horizontal section length, displacement, proppant dosage and production are significantly correlated. The relationship between fracturing fluid volume, section / cluster spacing, and stimulation volume and production is not obvious, and the fracture network width and production are irregular.

[0205] According to the existing relevant data, the development factors that affect the development law of horizontal wells are determined to be: drainage system, well shut-in time, gas-oil ratio, flowback rate, and pressure-sensitive effect. Based on the above factors, the impact of development factors on the development law of horizontal wells is analyzed, among which the drainage system is characterized by the flowback rate before the oil well appears oil, which is determined by the ratio of the flowback volume before the oil well appears oil and the number of days from the well opening to the oil appearing oil, the gas-oil ratio is characterized by the average of the five days before and after the critical time point in each production period, and the pressure-sensitive effect is characterized by the formation pressure difference after the oil well is put into production, that is, the difference between the formation pressure in each period and the original formation pressure.

[0206] Through data analysis, it can be concluded that regardless of the classification by drilling oil layer conditions or initial production capacity, the development rules are relatively consistent, and it can be concluded that the pressure-sensitive effect, horizontal section length, displacement, proppant dosage and production are significantly correlated; there is controversy over the correlation between fracturing fluid volume, fracture density, transformation volume, drainage system and production; the conclusion is that the fracture network width, well soaking time, gas-oil ratio, and flowback rate are not correlated with production.

[0207] Taking the production of horizontal wells for half a year as the evaluation standard, combined with the geological factors, engineering factors and development factors that affect the development law of horizontal well volume fracturing, the main controlling factors affecting the development law of horizontal well volume fracturing are finally determined as shown in Table 5. Table 5 is the result table of multi-factor analysis of factors affecting the development law of horizontal well volume fracturing.

[0208] Table 5

[0209]

[0210]

[0211] According to the multi-factor data analysis, it can be known that the pressure-sensitive effect has a more important impact on the production of the Ma 18 well area. Combined with the stress sensitivity test results of the experimental wells in the Ma 18 well area, Fig.12 express, Fig.12 FIG. 1 is a schematic diagram of the experimental results of stress sensitivity evaluation of the experimental well in this exemplary embodiment. Fig.12 It can be seen that with the increase of confining pressure, the permeability decreases significantly. According to the experimental results, it can be determined that the pressure-sensitive effect has a strong degree of damage to the formation.

[0212] From Table 5, the equivalent oil layer length encountered, the oil layer length encountered, the pressure-sensitive effect, the horizontal section length, the displacement, and the proppant dosage are the main controlling factors affecting the horizontal well development law. The experimental block is an abnormally high-pressure reservoir with deep burial depth, high original formation pressure, large formation pressure coefficient, small pore throat, complex pore structure, and low permeability. The pressure-sensitive effect is serious during the development process. As the oil well production continues, the formation pressure drops rapidly, and the pressure-sensitive effect becomes an important factor affecting the production capacity; increasing the displacement during the oil well production process is conducive to the formation of cracks, thereby improving the long-term production effect of the oil well, becoming an important factor affecting the production capacity; in terms of fracturing construction, increasing the horizontal section length and increasing the equivalent oil layer length drilling rate become the key factors affecting the production capacity.

[0213] It is worth noting that, after certain optimization, this embodiment is not only applicable to conglomerate tight oil reservoirs, but can also be extended to shale oil, carbonate, volcanic rock and other oil reservoirs.

[0214] Example:

[0215] Table 6

[0216]

[0217] From Table 6 (Results of multi-factor analysis of factors affecting the development law of horizontal well volume fracturing), it can be seen that the drilling equivalent oil layer length, drilling oil layer length, pressure-sensitive effect, horizontal section length, displacement, and proppant dosage are the main controlling factors affecting the development law of horizontal wells. The experimental block is an abnormally high-pressure reservoir with deep burial depth, high original formation pressure, large formation pressure coefficient, small pore throat, complex pore structure, and low permeability. The pressure-sensitive effect is serious during the development process. As the oil well production continues, the formation pressure drops rapidly, and the pressure-sensitive effect becomes an important factor affecting production capacity; increasing the displacement during the production of the oil well is conducive to the formation of cracks, thereby improving the long-term production effect of the oil well, becoming an important factor affecting production capacity; in terms of fracturing construction, increasing the length of the horizontal section and increasing the drilling rate of the equivalent oil layer length become the key factors affecting production capacity.

[0218] Fig.13 is a block diagram of a production well productivity influencing factor analysis system of the exemplary embodiment, such as Fig.13 As shown, an exemplary embodiment of the present disclosure provides a production well productivity influencing factor analysis system, comprising:

[0219] Establishing unit 10, for establishing a production well productivity evaluation model;

[0220] The determination unit 20 is used to analyze the fracturing database of the production wells and determine the correlation between various factors affecting the production capacity of the production wells and the production capacity of the production wells;

[0221] A sorting unit 30, used to sort the influencing factors according to the magnitude of the correlation;

[0222] The evaluation unit 40 is used to select the first n influencing factors to input into the production well productivity evaluation model, and output the evaluation result of the production well productivity, n≥1.

[0223] Exemplarily, the establishing unit 10 includes:

[0224] The first calculation module is used to calculate the oil production per meter of each type of oil layer;

[0225] The equivalent module is used to divide the thickness of each oil layer by the oil production per meter of the corresponding oil layer to make the production capacity of each oil layer equivalent;

[0226] Conversion module, used to convert the thickness of various oil layers according to the production capacity of various oil layers;

[0227] Build a module for building multiple regression models;

[0228] A module is formed, which is used to input the converted oil layer thickness into a multivariate regression model to form a production well capacity evaluation model.

[0229] Exemplarily, factors affecting the productivity of production wells include geological factors and engineering factors. Geological factors include horizontal section length, oil layer thickness, permeability, formation pressure and porosity; engineering factors include fracturing stage, fracture length, fracturing fluid and sand addition amount.

[0230] Exemplarily, the determining unit 20 includes:

[0231] Determination module, used to determine the analysis sequence: determine the production well capacity data parent sequence X0 and the influencing factor data subsequence X i (i=1,2,…,n), i represents the i-th factor affecting production capacity, forming the analysis sequence matrix: In the formula, N means that there are N values ​​in the i-th influencing factor, n means there are n factors affecting production capacity, and X n (N) indicates the size of the Nth value in the nth influencing factor;

[0232] The processing module is used for dimensionless processing of the original data: the average value of each sequence is calculated, and the data column obtained by dividing the average value by each original data in the corresponding sequence is recorded as the averaged sequence: In the formula, i represents the i-th factor affecting production capacity, k represents the k-th moment value of the i-th influencing factor, N represents the number of N values ​​in the i-th influencing factor, and x′ i (k) represents the k-th moment value of the i-th sequence of factors affecting capacity divided by the mean of the i-th sequence of factors affecting capacity;

[0233] The second calculation module is used to calculate the absolute value Δ of the difference between the parent sequence and each subsequence at each time point 0i (k):Δ 0i (k)=|X0(k)-X i (k)|, where: Δ 0i (k) is |X at time k i -X0|value, k represents the k-th moment value in the i-th influencing factor;

[0234] The third calculation module is used to calculate the relationship between the parent sequence X0 and each subsequence X at each time point. i The correlation coefficient is calculated as follows: In the formula, ε 0i (k) represents the parent sequence X0 and each subsequence X i The correlation coefficient, Δ 0i (k) represents |X at time k i -X0| value, △min represents the minimum difference between the parent sequence and the subsequence, △max represents the maximum difference between the parent sequence and the subsequence, and ρ is the resolution coefficient;

[0235] The first solution module is used to solve the correlation coefficient matrix: Where: ε 0i (k) represents the parent sequence X0 and each subsequence X i The correlation coefficient, ε 0n (N) represents the correlation coefficient between the parent sequence X0 and the nth subsequence, and N represents that there are N values ​​in the i-th influencing factor;

[0236] The second solution module is used to solve the correlation degree: the correlation degree of the two sequences is calculated by the average value of the correlation coefficients of the two sequences at each moment. The calculation formula is: In the formula, γ i represents the correlation between the parent sequence and the i-th influencing factor, N represents that there are N values ​​in the i-th influencing factor, ε 0i (k) represents the parent sequence X0 and each subsequence X i The correlation coefficient, k represents the k-th moment value of the i-th influencing factor;

[0237] The third solution module is used to solve the parameter weight: the weight of each influencing factor is calculated using the correlation degree, and the calculation formula is: Where W i represents the weight of the i-th influencing factor, γ i It represents the correlation between the parent sequence and the i-th influencing factor, and n represents that there are n factors affecting production capacity.

[0238] Fig.14 Schematic diagram of the structure of a production well productivity influencing factor analysis device of this exemplary embodiment. Fig.14As shown, corresponding to the above-mentioned production well productivity influencing factor analysis method, the present invention also provides a production well productivity influencing factor analysis device. Since the embodiment of the device is similar to the above-mentioned method embodiment, the description is relatively simple. For relevant parts, please refer to the description of the above-mentioned method embodiment part. The device described below is only schematic. The device may include: a processor (processor) 1, a memory (memory) 2 and a communication bus (i.e., the above-mentioned device bus) and a search engine, wherein the processor 1 and the memory 2 communicate with each other through the communication bus and communicate with the outside through the communication interface. The processor 1 can call the logic instructions in the memory 2 to execute the production well productivity influencing factor analysis method.

[0239] In addition, the logic instructions in the above-mentioned memory 2 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: a storage chip, a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, and other media that can store program codes.

[0240] On the other hand, an embodiment of the present invention further provides a processor-readable storage medium, on which a computer program 3 is stored. When the computer program 3 is executed by the processor 1, the production well productivity influencing factor analysis method provided in the above embodiments is implemented.

[0241] The processor-readable storage medium can be any available medium or data storage device that can be accessed by the processor 1, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid-state drive (SSD)), etc.

[0242] The above are only preferred implementations of the present disclosure, and the protection scope of the present disclosure is not limited to the above embodiments. All technical solutions under the concept of the present disclosure belong to the protection scope of the present disclosure. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present disclosure should be regarded as the protection scope of the present disclosure.

Claims

1. A method for analyzing factors affecting production well productivity, characterized in that: include: Establish a production well productivity evaluation model; Analyze the fracturing database of production wells to determine the correlation between various factors affecting the production capacity of production wells and the production capacity of production wells; Sort the influencing factors according to the degree of correlation; Select the first n influencing factors and input them into the production well capacity evaluation model, and output the evaluation result of the production well capacity, n≥1.

2. The method for analyzing factors affecting production well productivity according to claim 1, characterized in that: The establishment of the production well productivity evaluation model comprises: Calculate the oil production per meter of each type of oil layer; Divide the thickness of each oil layer by the oil production per meter of the corresponding oil layer to make the production capacity of each oil layer equivalent; Calculate the thickness of various oil layers according to their production capacity; Establish multiple regression models; The converted oil layer thickness is input into the multivariate regression model to form a production well productivity evaluation model.

3. The method for analyzing factors affecting production well productivity according to claim 2, characterized in that: The factors affecting the productivity of production wells include geological factors and engineering factors. The geological factors include horizontal section length, oil layer thickness, permeability, formation pressure and porosity, and the engineering factors include fracturing stage, fracture length, fracturing fluid and sand addition amount.

4. The method for analyzing factors affecting production well productivity according to any one of claims 1 to 3, characterized in that: The analysis of the fracturing database of the production wells to determine the correlation between the various factors affecting the production capacity of the production wells and the production capacity of the production wells includes: Determine the analysis sequence: Determine the production well capacity data parent sequence X0 and the influencing factor data subsequence X i (i=1,2,…,n), i represents the i-th factor affecting production capacity, forming the analysis sequence matrix: In the formula, N means that there are N values ​​in the i-th influencing factor, n means there are n factors affecting production capacity, and X n (N) indicates the size of the Nth value in the nth influencing factor; Dimensionless processing of original data: Find the average value of each sequence, divide the average value by each original data in the corresponding sequence, and the obtained data column is recorded as the averaged sequence: In the formula, i represents the i-th factor affecting production capacity, k represents the k-th moment value of the i-th influencing factor, N represents the number of N values ​​in the i-th influencing factor, and x′ i (k) represents the k-th moment value of the i-th sequence of factors affecting capacity divided by the mean of the i-th sequence of factors affecting capacity; Calculate the absolute value Δ of the difference between the parent sequence and each subsequence at each time point 0i (k):Δ 0i (k)=|X0(k)-X i (k)|, where: Δ 0i (k) is |X at time k i -X0|value, k represents the k-th moment value in the i-th influencing factor; Calculate the relationship between the parent sequence X0 and each subsequence X at each time point i The correlation coefficient is calculated as follows: In the formula, ε 0i (k) represents the parent sequence X0 and each subsequence X i The correlation coefficient, Δ 0i (k) represents |X at time k i -X0| value, △min represents the minimum difference between the parent sequence and the subsequence, △max represents the maximum difference between the parent sequence and the subsequence, and ρ is the resolution coefficient; Solve for the correlation coefficient matrix: Where: ε 0i (k) represents the parent sequence X0 and each subsequence X i The correlation coefficient, ε 0n (N) represents the correlation coefficient between the parent sequence X0 and the nth subsequence, N represents that there are N values ​​in the i-th influencing factor, and n represents that there are n factors affecting production capacity; Solving the correlation degree: Calculate the correlation degree of the two sequences by taking the average value of the correlation coefficients of the two sequences at each moment. The calculation formula is: In the formula, γ i represents the correlation between the parent sequence and the i-th influencing factor, N represents that there are N values ​​in the i-th influencing factor, ε 0i (k) represents the parent sequence X0 and each subsequence X i The correlation coefficient, k represents the k-th moment value of the i-th influencing factor; Solving parameter weights: Use the correlation to calculate the weights of each influencing factor. The calculation formula is: Where W i represents the weight of the i-th influencing factor, γ i It represents the correlation between the parent sequence and the i-th influencing factor, and n represents that there are n factors affecting production capacity.

5. A production well productivity influencing factor analysis system, characterized in that: include: Establishing a unit for establishing a production well productivity evaluation model; A determination unit, used to analyze the fracturing database of the production wells and determine the correlation between various factors affecting the production capacity of the production wells and the production capacity of the production wells; A sorting unit is used to sort the influencing factors according to the magnitude of the correlation; The evaluation unit is used to select the first n influencing factors to input into the production well capacity evaluation model, and output the evaluation result of the production well capacity, n≥1.

6. The production well productivity influencing factor analysis system according to claim 5, characterized in that: The establishing unit comprises: The first calculation module is used to calculate the oil production per meter of each type of oil layer; The equivalent module is used to divide the thickness of each oil layer by the oil production per meter of the corresponding oil layer to make the production capacity of each oil layer equivalent; Conversion module, used to convert the thickness of various oil layers according to the production capacity of various oil layers; Build a module for building multiple regression models; A module is formed, which is used to input the converted oil layer thickness into a multivariate regression model to form a production well capacity evaluation model.

7. The production well productivity influencing factor analysis system according to claim 6, characterized in that: The factors affecting the productivity of production wells include geological factors and engineering factors. The geological factors include horizontal section length, oil layer thickness, permeability, formation pressure and porosity, and the engineering factors include fracturing stage, fracture length, fracturing fluid and sand addition amount.

8. The production well productivity influencing factor analysis system according to any one of claims 5 to 7, characterized in that: The determining unit comprises: Determination module, used to determine the analysis sequence: determine the production well capacity data parent sequence X0 and the influencing factor data subsequence X i (i=1,2,…,n), i represents the i-th factor affecting production capacity, forming the analysis sequence matrix: In the formula, N means that there are N values ​​in the i-th influencing factor, n means there are n factors affecting production capacity, and X n (N) indicates the size of the Nth value in the nth influencing factor; The processing module is used for dimensionless processing of the original data: the average value of each sequence is calculated, and the data column obtained by dividing the average value by each original data in the corresponding sequence is recorded as the averaged sequence: In the formula, i represents the i-th factor affecting production capacity, k represents the k-th moment value of the i-th influencing factor, N represents the number of N values ​​in the i-th influencing factor, and x′ i (k) represents the k-th moment value of the i-th sequence of factors affecting capacity divided by the mean of the i-th sequence of factors affecting capacity; The second calculation module is used to calculate the absolute value Δ of the difference between the parent sequence and each subsequence at each time point 0i (k):Δ 0i (k)=|X0(k)-X i (k)|, where: Δ 0i (k) is |X at time k i -X0|value, k represents the k-th moment value in the i-th influencing factor; The third calculation module is used to calculate the relationship between the parent sequence X0 and each subsequence X at each time point. i The correlation coefficient is calculated as follows: In the formula, ε 0i (k) represents the parent sequence X0 and each subsequence X i The correlation coefficient, Δ 0i (k) represents |X at time k i -X0| value, △min represents the minimum difference between the parent sequence and the subsequence, △max represents the maximum difference between the parent sequence and the subsequence, and ρ is the resolution coefficient; The first solution module is used to solve the correlation coefficient matrix: Where: ε 0i (k) represents the parent sequence X0 and each subsequence X i The correlation coefficient, ε 0n (N) represents the correlation coefficient between the parent sequence X0 and the nth subsequence, N represents that there are N values ​​in the i-th influencing factor, and n represents that there are n factors affecting production capacity; The second solution module is used to solve the correlation degree: the correlation degree of the two sequences is calculated by the average value of the correlation coefficients of the two sequences at each moment. The calculation formula is: In the formula, γ i represents the correlation between the parent sequence and the i-th influencing factor, N represents that there are N values ​​in the i-th influencing factor, ε 0i (k) represents the parent sequence X0 and each subsequence X i The correlation coefficient, k represents the k-th moment value of the i-th influencing factor; The third solution module is used to solve the parameter weight: the weight of each influencing factor is calculated using the correlation degree, and the calculation formula is: Where W i represents the weight of the i-th influencing factor, γ i It represents the correlation between the parent sequence and the i-th influencing factor, and n represents that there are n factors affecting production capacity.

9. A production well productivity influencing factor analysis device, characterized in that: include: Processor and memory; The memory is used to store a computer program, and the processor calls the computer program stored in the memory to execute the method for analyzing factors affecting production well productivity as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is enabled to execute the method for analyzing factors affecting production well productivity as described in any one of claims 1 to 4.

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